Evaluating and Optimizing CNN–Transformer Architectures for Musculoskeletal Disease Classification

This study examines the impact of dataset dimensionality on deep learning performance in musculoskeletal disease detection, focusing on osteoporosis and rheumatoid arthritis. Using over 200,000 annotated X-ray, DXA, and MRI images, the performance of Vision Transformer (ViT), ConvNeXt, and Swin Transformer models was systematically evaluated in terms of scalability, robustness, and multi-modal integration. Results demonstrate that increasing dataset scale significantly enhances model generalization, with Swin Transformer achieving the best performance (AUC = 0.94, p < 0.001). These findings underscore the critical role of self-attention mechanisms and model scaling strategies in medical image classification, providing new benchmarks for dataset requirements and guiding the development of more reliable AI-driven diagnostic systems. Furthermore, the study emphasizes the necessity of large, diverse datasets to mitigate overfitting and improve real-world applicability. It also highlights the potential of hybrid architectures for integrating multi-source medical data. Overall, this research contributes to advancing explainable and scalable AI solutions for musculoskeletal imaging in clinical practice.